Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Kuo-Hsin Tseng, Jin-Wei Liang, Wunching Chang and Shyh-Chin Huang
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Kuo-Hsin Tseng: Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan
Jin-Wei Liang: Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan
Wunching Chang: Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan
Shyh-Chin Huang: Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan

Energies, 2015, vol. 8, issue 4, 1-19

Abstract: Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (V dis ) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using V dis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients.

Keywords: battery cycle life; battery state of health; battery reliability; particle swarm optimization; battery remaining useful life estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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